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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 3761–3770 of 4002 papers

TitleStatusHype
Emotion Enriched Retrofitted Word Embeddings—0
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling—0
Empirical Analysis of Image Caption Generation using Deep Learning—0
Empirical Autopsy of Deep Video Captioning Frameworks—0
Empirical Study of Diachronic Word Embeddings for Scarce Data—0
Employing Word Representations and Regularization for Domain Adaptation of Relation Extraction—0
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts—0
Empty Category Detection using Path Features and Distributed Case Frames—0
Enabling Cognitive Intelligence Queries in Relational Databases using Low-dimensional Word Embeddings—0
Enabling Open-World Specification Mining via Unsupervised Learning—0
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